Intelegentny_Pszczelarz/.venv/Lib/site-packages/sklearn/tests/test_isotonic.py
2023-06-19 00:49:18 +02:00

705 lines
22 KiB
Python

import warnings
import numpy as np
import pickle
import copy
import pytest
import sklearn
from sklearn.datasets import make_regression
from sklearn.isotonic import (
check_increasing,
isotonic_regression,
IsotonicRegression,
_make_unique,
)
from sklearn.utils.validation import check_array
from sklearn.utils._testing import (
assert_allclose,
assert_array_equal,
assert_array_almost_equal,
)
from sklearn.utils import shuffle
from scipy.special import expit
def test_permutation_invariance():
# check that fit is permutation invariant.
# regression test of missing sorting of sample-weights
ir = IsotonicRegression()
x = [1, 2, 3, 4, 5, 6, 7]
y = [1, 41, 51, 1, 2, 5, 24]
sample_weight = [1, 2, 3, 4, 5, 6, 7]
x_s, y_s, sample_weight_s = shuffle(x, y, sample_weight, random_state=0)
y_transformed = ir.fit_transform(x, y, sample_weight=sample_weight)
y_transformed_s = ir.fit(x_s, y_s, sample_weight=sample_weight_s).transform(x)
assert_array_equal(y_transformed, y_transformed_s)
def test_check_increasing_small_number_of_samples():
x = [0, 1, 2]
y = [1, 1.1, 1.05]
with warnings.catch_warnings():
warnings.simplefilter("error", UserWarning)
is_increasing = check_increasing(x, y)
assert is_increasing
def test_check_increasing_up():
x = [0, 1, 2, 3, 4, 5]
y = [0, 1.5, 2.77, 8.99, 8.99, 50]
# Check that we got increasing=True and no warnings
with warnings.catch_warnings():
warnings.simplefilter("error", UserWarning)
is_increasing = check_increasing(x, y)
assert is_increasing
def test_check_increasing_up_extreme():
x = [0, 1, 2, 3, 4, 5]
y = [0, 1, 2, 3, 4, 5]
# Check that we got increasing=True and no warnings
with warnings.catch_warnings():
warnings.simplefilter("error", UserWarning)
is_increasing = check_increasing(x, y)
assert is_increasing
def test_check_increasing_down():
x = [0, 1, 2, 3, 4, 5]
y = [0, -1.5, -2.77, -8.99, -8.99, -50]
# Check that we got increasing=False and no warnings
with warnings.catch_warnings():
warnings.simplefilter("error", UserWarning)
is_increasing = check_increasing(x, y)
assert not is_increasing
def test_check_increasing_down_extreme():
x = [0, 1, 2, 3, 4, 5]
y = [0, -1, -2, -3, -4, -5]
# Check that we got increasing=False and no warnings
with warnings.catch_warnings():
warnings.simplefilter("error", UserWarning)
is_increasing = check_increasing(x, y)
assert not is_increasing
def test_check_ci_warn():
x = [0, 1, 2, 3, 4, 5]
y = [0, -1, 2, -3, 4, -5]
# Check that we got increasing=False and CI interval warning
msg = "interval"
with pytest.warns(UserWarning, match=msg):
is_increasing = check_increasing(x, y)
assert not is_increasing
def test_isotonic_regression():
y = np.array([3, 7, 5, 9, 8, 7, 10])
y_ = np.array([3, 6, 6, 8, 8, 8, 10])
assert_array_equal(y_, isotonic_regression(y))
y = np.array([10, 0, 2])
y_ = np.array([4, 4, 4])
assert_array_equal(y_, isotonic_regression(y))
x = np.arange(len(y))
ir = IsotonicRegression(y_min=0.0, y_max=1.0)
ir.fit(x, y)
assert_array_equal(ir.fit(x, y).transform(x), ir.fit_transform(x, y))
assert_array_equal(ir.transform(x), ir.predict(x))
# check that it is immune to permutation
perm = np.random.permutation(len(y))
ir = IsotonicRegression(y_min=0.0, y_max=1.0)
assert_array_equal(ir.fit_transform(x[perm], y[perm]), ir.fit_transform(x, y)[perm])
assert_array_equal(ir.transform(x[perm]), ir.transform(x)[perm])
# check we don't crash when all x are equal:
ir = IsotonicRegression()
assert_array_equal(ir.fit_transform(np.ones(len(x)), y), np.mean(y))
def test_isotonic_regression_ties_min():
# Setup examples with ties on minimum
x = [1, 1, 2, 3, 4, 5]
y = [1, 2, 3, 4, 5, 6]
y_true = [1.5, 1.5, 3, 4, 5, 6]
# Check that we get identical results for fit/transform and fit_transform
ir = IsotonicRegression()
ir.fit(x, y)
assert_array_equal(ir.fit(x, y).transform(x), ir.fit_transform(x, y))
assert_array_equal(y_true, ir.fit_transform(x, y))
def test_isotonic_regression_ties_max():
# Setup examples with ties on maximum
x = [1, 2, 3, 4, 5, 5]
y = [1, 2, 3, 4, 5, 6]
y_true = [1, 2, 3, 4, 5.5, 5.5]
# Check that we get identical results for fit/transform and fit_transform
ir = IsotonicRegression()
ir.fit(x, y)
assert_array_equal(ir.fit(x, y).transform(x), ir.fit_transform(x, y))
assert_array_equal(y_true, ir.fit_transform(x, y))
def test_isotonic_regression_ties_secondary_():
"""
Test isotonic regression fit, transform and fit_transform
against the "secondary" ties method and "pituitary" data from R
"isotone" package, as detailed in: J. d. Leeuw, K. Hornik, P. Mair,
Isotone Optimization in R: Pool-Adjacent-Violators Algorithm
(PAVA) and Active Set Methods
Set values based on pituitary example and
the following R command detailed in the paper above:
> library("isotone")
> data("pituitary")
> res1 <- gpava(pituitary$age, pituitary$size, ties="secondary")
> res1$x
`isotone` version: 1.0-2, 2014-09-07
R version: R version 3.1.1 (2014-07-10)
"""
x = [8, 8, 8, 10, 10, 10, 12, 12, 12, 14, 14]
y = [21, 23.5, 23, 24, 21, 25, 21.5, 22, 19, 23.5, 25]
y_true = [
22.22222,
22.22222,
22.22222,
22.22222,
22.22222,
22.22222,
22.22222,
22.22222,
22.22222,
24.25,
24.25,
]
# Check fit, transform and fit_transform
ir = IsotonicRegression()
ir.fit(x, y)
assert_array_almost_equal(ir.transform(x), y_true, 4)
assert_array_almost_equal(ir.fit_transform(x, y), y_true, 4)
def test_isotonic_regression_with_ties_in_differently_sized_groups():
"""
Non-regression test to handle issue 9432:
https://github.com/scikit-learn/scikit-learn/issues/9432
Compare against output in R:
> library("isotone")
> x <- c(0, 1, 1, 2, 3, 4)
> y <- c(0, 0, 1, 0, 0, 1)
> res1 <- gpava(x, y, ties="secondary")
> res1$x
`isotone` version: 1.1-0, 2015-07-24
R version: R version 3.3.2 (2016-10-31)
"""
x = np.array([0, 1, 1, 2, 3, 4])
y = np.array([0, 0, 1, 0, 0, 1])
y_true = np.array([0.0, 0.25, 0.25, 0.25, 0.25, 1.0])
ir = IsotonicRegression()
ir.fit(x, y)
assert_array_almost_equal(ir.transform(x), y_true)
assert_array_almost_equal(ir.fit_transform(x, y), y_true)
def test_isotonic_regression_reversed():
y = np.array([10, 9, 10, 7, 6, 6.1, 5])
y_ = IsotonicRegression(increasing=False).fit_transform(np.arange(len(y)), y)
assert_array_equal(np.ones(y_[:-1].shape), ((y_[:-1] - y_[1:]) >= 0))
def test_isotonic_regression_auto_decreasing():
# Set y and x for decreasing
y = np.array([10, 9, 10, 7, 6, 6.1, 5])
x = np.arange(len(y))
# Create model and fit_transform
ir = IsotonicRegression(increasing="auto")
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
y_ = ir.fit_transform(x, y)
# work-around for pearson divide warnings in scipy <= 0.17.0
assert all(["invalid value encountered in " in str(warn.message) for warn in w])
# Check that relationship decreases
is_increasing = y_[0] < y_[-1]
assert not is_increasing
def test_isotonic_regression_auto_increasing():
# Set y and x for decreasing
y = np.array([5, 6.1, 6, 7, 10, 9, 10])
x = np.arange(len(y))
# Create model and fit_transform
ir = IsotonicRegression(increasing="auto")
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
y_ = ir.fit_transform(x, y)
# work-around for pearson divide warnings in scipy <= 0.17.0
assert all(["invalid value encountered in " in str(warn.message) for warn in w])
# Check that relationship increases
is_increasing = y_[0] < y_[-1]
assert is_increasing
def test_assert_raises_exceptions():
ir = IsotonicRegression()
rng = np.random.RandomState(42)
msg = "Found input variables with inconsistent numbers of samples"
with pytest.raises(ValueError, match=msg):
ir.fit([0, 1, 2], [5, 7, 3], [0.1, 0.6])
with pytest.raises(ValueError, match=msg):
ir.fit([0, 1, 2], [5, 7])
msg = "X should be a 1d array"
with pytest.raises(ValueError, match=msg):
ir.fit(rng.randn(3, 10), [0, 1, 2])
msg = "Isotonic regression input X should be a 1d array"
with pytest.raises(ValueError, match=msg):
ir.transform(rng.randn(3, 10))
def test_isotonic_sample_weight_parameter_default_value():
# check if default value of sample_weight parameter is one
ir = IsotonicRegression()
# random test data
rng = np.random.RandomState(42)
n = 100
x = np.arange(n)
y = rng.randint(-50, 50, size=(n,)) + 50.0 * np.log(1 + np.arange(n))
# check if value is correctly used
weights = np.ones(n)
y_set_value = ir.fit_transform(x, y, sample_weight=weights)
y_default_value = ir.fit_transform(x, y)
assert_array_equal(y_set_value, y_default_value)
def test_isotonic_min_max_boundaries():
# check if min value is used correctly
ir = IsotonicRegression(y_min=2, y_max=4)
n = 6
x = np.arange(n)
y = np.arange(n)
y_test = [2, 2, 2, 3, 4, 4]
y_result = np.round(ir.fit_transform(x, y))
assert_array_equal(y_result, y_test)
def test_isotonic_sample_weight():
ir = IsotonicRegression()
x = [1, 2, 3, 4, 5, 6, 7]
y = [1, 41, 51, 1, 2, 5, 24]
sample_weight = [1, 2, 3, 4, 5, 6, 7]
expected_y = [1, 13.95, 13.95, 13.95, 13.95, 13.95, 24]
received_y = ir.fit_transform(x, y, sample_weight=sample_weight)
assert_array_equal(expected_y, received_y)
def test_isotonic_regression_oob_raise():
# Set y and x
y = np.array([3, 7, 5, 9, 8, 7, 10])
x = np.arange(len(y))
# Create model and fit
ir = IsotonicRegression(increasing="auto", out_of_bounds="raise")
ir.fit(x, y)
# Check that an exception is thrown
msg = "in x_new is below the interpolation range"
with pytest.raises(ValueError, match=msg):
ir.predict([min(x) - 10, max(x) + 10])
def test_isotonic_regression_oob_clip():
# Set y and x
y = np.array([3, 7, 5, 9, 8, 7, 10])
x = np.arange(len(y))
# Create model and fit
ir = IsotonicRegression(increasing="auto", out_of_bounds="clip")
ir.fit(x, y)
# Predict from training and test x and check that min/max match.
y1 = ir.predict([min(x) - 10, max(x) + 10])
y2 = ir.predict(x)
assert max(y1) == max(y2)
assert min(y1) == min(y2)
def test_isotonic_regression_oob_nan():
# Set y and x
y = np.array([3, 7, 5, 9, 8, 7, 10])
x = np.arange(len(y))
# Create model and fit
ir = IsotonicRegression(increasing="auto", out_of_bounds="nan")
ir.fit(x, y)
# Predict from training and test x and check that we have two NaNs.
y1 = ir.predict([min(x) - 10, max(x) + 10])
assert sum(np.isnan(y1)) == 2
def test_isotonic_regression_pickle():
y = np.array([3, 7, 5, 9, 8, 7, 10])
x = np.arange(len(y))
# Create model and fit
ir = IsotonicRegression(increasing="auto", out_of_bounds="clip")
ir.fit(x, y)
ir_ser = pickle.dumps(ir, pickle.HIGHEST_PROTOCOL)
ir2 = pickle.loads(ir_ser)
np.testing.assert_array_equal(ir.predict(x), ir2.predict(x))
def test_isotonic_duplicate_min_entry():
x = [0, 0, 1]
y = [0, 0, 1]
ir = IsotonicRegression(increasing=True, out_of_bounds="clip")
ir.fit(x, y)
all_predictions_finite = np.all(np.isfinite(ir.predict(x)))
assert all_predictions_finite
def test_isotonic_ymin_ymax():
# Test from @NelleV's issue:
# https://github.com/scikit-learn/scikit-learn/issues/6921
x = np.array(
[
1.263,
1.318,
-0.572,
0.307,
-0.707,
-0.176,
-1.599,
1.059,
1.396,
1.906,
0.210,
0.028,
-0.081,
0.444,
0.018,
-0.377,
-0.896,
-0.377,
-1.327,
0.180,
]
)
y = isotonic_regression(x, y_min=0.0, y_max=0.1)
assert np.all(y >= 0)
assert np.all(y <= 0.1)
# Also test decreasing case since the logic there is different
y = isotonic_regression(x, y_min=0.0, y_max=0.1, increasing=False)
assert np.all(y >= 0)
assert np.all(y <= 0.1)
# Finally, test with only one bound
y = isotonic_regression(x, y_min=0.0, increasing=False)
assert np.all(y >= 0)
def test_isotonic_zero_weight_loop():
# Test from @ogrisel's issue:
# https://github.com/scikit-learn/scikit-learn/issues/4297
# Get deterministic RNG with seed
rng = np.random.RandomState(42)
# Create regression and samples
regression = IsotonicRegression()
n_samples = 50
x = np.linspace(-3, 3, n_samples)
y = x + rng.uniform(size=n_samples)
# Get some random weights and zero out
w = rng.uniform(size=n_samples)
w[5:8] = 0
regression.fit(x, y, sample_weight=w)
# This will hang in failure case.
regression.fit(x, y, sample_weight=w)
def test_fast_predict():
# test that the faster prediction change doesn't
# affect out-of-sample predictions:
# https://github.com/scikit-learn/scikit-learn/pull/6206
rng = np.random.RandomState(123)
n_samples = 10**3
# X values over the -10,10 range
X_train = 20.0 * rng.rand(n_samples) - 10
y_train = (
np.less(rng.rand(n_samples), expit(X_train)).astype("int64").astype("float64")
)
weights = rng.rand(n_samples)
# we also want to test that everything still works when some weights are 0
weights[rng.rand(n_samples) < 0.1] = 0
slow_model = IsotonicRegression(y_min=0, y_max=1, out_of_bounds="clip")
fast_model = IsotonicRegression(y_min=0, y_max=1, out_of_bounds="clip")
# Build interpolation function with ALL input data, not just the
# non-redundant subset. The following 2 lines are taken from the
# .fit() method, without removing unnecessary points
X_train_fit, y_train_fit = slow_model._build_y(
X_train, y_train, sample_weight=weights, trim_duplicates=False
)
slow_model._build_f(X_train_fit, y_train_fit)
# fit with just the necessary data
fast_model.fit(X_train, y_train, sample_weight=weights)
X_test = 20.0 * rng.rand(n_samples) - 10
y_pred_slow = slow_model.predict(X_test)
y_pred_fast = fast_model.predict(X_test)
assert_array_equal(y_pred_slow, y_pred_fast)
def test_isotonic_copy_before_fit():
# https://github.com/scikit-learn/scikit-learn/issues/6628
ir = IsotonicRegression()
copy.copy(ir)
def test_isotonic_dtype():
y = [2, 1, 4, 3, 5]
weights = np.array([0.9, 0.9, 0.9, 0.9, 0.9], dtype=np.float64)
reg = IsotonicRegression()
for dtype in (np.int32, np.int64, np.float32, np.float64):
for sample_weight in (None, weights.astype(np.float32), weights):
y_np = np.array(y, dtype=dtype)
expected_dtype = check_array(
y_np, dtype=[np.float64, np.float32], ensure_2d=False
).dtype
res = isotonic_regression(y_np, sample_weight=sample_weight)
assert res.dtype == expected_dtype
X = np.arange(len(y)).astype(dtype)
reg.fit(X, y_np, sample_weight=sample_weight)
res = reg.predict(X)
assert res.dtype == expected_dtype
@pytest.mark.parametrize("y_dtype", [np.int32, np.int64, np.float32, np.float64])
def test_isotonic_mismatched_dtype(y_dtype):
# regression test for #15004
# check that data are converted when X and y dtype differ
reg = IsotonicRegression()
y = np.array([2, 1, 4, 3, 5], dtype=y_dtype)
X = np.arange(len(y), dtype=np.float32)
reg.fit(X, y)
assert reg.predict(X).dtype == X.dtype
def test_make_unique_dtype():
x_list = [2, 2, 2, 3, 5]
for dtype in (np.float32, np.float64):
x = np.array(x_list, dtype=dtype)
y = x.copy()
w = np.ones_like(x)
x, y, w = _make_unique(x, y, w)
assert_array_equal(x, [2, 3, 5])
@pytest.mark.parametrize("dtype", [np.float64, np.float32])
def test_make_unique_tolerance(dtype):
# Check that equality takes account of np.finfo tolerance
x = np.array([0, 1e-16, 1, 1 + 1e-14], dtype=dtype)
y = x.copy()
w = np.ones_like(x)
x, y, w = _make_unique(x, y, w)
if dtype == np.float64:
x_out = np.array([0, 1, 1 + 1e-14])
else:
x_out = np.array([0, 1])
assert_array_equal(x, x_out)
def test_isotonic_make_unique_tolerance():
# Check that averaging of targets for duplicate X is done correctly,
# taking into account tolerance
X = np.array([0, 1, 1 + 1e-16, 2], dtype=np.float64)
y = np.array([0, 1, 2, 3], dtype=np.float64)
ireg = IsotonicRegression().fit(X, y)
y_pred = ireg.predict([0, 0.5, 1, 1.5, 2])
assert_array_equal(y_pred, np.array([0, 0.75, 1.5, 2.25, 3]))
assert_array_equal(ireg.X_thresholds_, np.array([0.0, 1.0, 2.0]))
assert_array_equal(ireg.y_thresholds_, np.array([0.0, 1.5, 3.0]))
def test_isotonic_non_regression_inf_slope():
# Non-regression test to ensure that inf values are not returned
# see: https://github.com/scikit-learn/scikit-learn/issues/10903
X = np.array([0.0, 4.1e-320, 4.4e-314, 1.0])
y = np.array([0.42, 0.42, 0.44, 0.44])
ireg = IsotonicRegression().fit(X, y)
y_pred = ireg.predict(np.array([0, 2.1e-319, 5.4e-316, 1e-10]))
assert np.all(np.isfinite(y_pred))
@pytest.mark.parametrize("increasing", [True, False])
def test_isotonic_thresholds(increasing):
rng = np.random.RandomState(42)
n_samples = 30
X = rng.normal(size=n_samples)
y = rng.normal(size=n_samples)
ireg = IsotonicRegression(increasing=increasing).fit(X, y)
X_thresholds, y_thresholds = ireg.X_thresholds_, ireg.y_thresholds_
assert X_thresholds.shape == y_thresholds.shape
# Input thresholds are a strict subset of the training set (unless
# the data is already strictly monotonic which is not the case with
# this random data)
assert X_thresholds.shape[0] < X.shape[0]
assert np.in1d(X_thresholds, X).all()
# Output thresholds lie in the range of the training set:
assert y_thresholds.max() <= y.max()
assert y_thresholds.min() >= y.min()
assert all(np.diff(X_thresholds) > 0)
if increasing:
assert all(np.diff(y_thresholds) >= 0)
else:
assert all(np.diff(y_thresholds) <= 0)
def test_input_shape_validation():
# Test from #15012
# Check that IsotonicRegression can handle 2darray with only 1 feature
X = np.arange(10)
X_2d = X.reshape(-1, 1)
y = np.arange(10)
iso_reg = IsotonicRegression().fit(X, y)
iso_reg_2d = IsotonicRegression().fit(X_2d, y)
assert iso_reg.X_max_ == iso_reg_2d.X_max_
assert iso_reg.X_min_ == iso_reg_2d.X_min_
assert iso_reg.y_max == iso_reg_2d.y_max
assert iso_reg.y_min == iso_reg_2d.y_min
assert_array_equal(iso_reg.X_thresholds_, iso_reg_2d.X_thresholds_)
assert_array_equal(iso_reg.y_thresholds_, iso_reg_2d.y_thresholds_)
y_pred1 = iso_reg.predict(X)
y_pred2 = iso_reg_2d.predict(X_2d)
assert_allclose(y_pred1, y_pred2)
def test_isotonic_2darray_more_than_1_feature():
# Ensure IsotonicRegression raises error if input has more than 1 feature
X = np.arange(10)
X_2d = np.c_[X, X]
y = np.arange(10)
msg = "should be a 1d array or 2d array with 1 feature"
with pytest.raises(ValueError, match=msg):
IsotonicRegression().fit(X_2d, y)
iso_reg = IsotonicRegression().fit(X, y)
with pytest.raises(ValueError, match=msg):
iso_reg.predict(X_2d)
with pytest.raises(ValueError, match=msg):
iso_reg.transform(X_2d)
def test_isotonic_regression_sample_weight_not_overwritten():
"""Check that calling fitting function of isotonic regression will not
overwrite `sample_weight`.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/20508
"""
X, y = make_regression(n_samples=10, n_features=1, random_state=41)
sample_weight_original = np.ones_like(y)
sample_weight_original[0] = 10
sample_weight_fit = sample_weight_original.copy()
isotonic_regression(y, sample_weight=sample_weight_fit)
assert_allclose(sample_weight_fit, sample_weight_original)
IsotonicRegression().fit(X, y, sample_weight=sample_weight_fit)
assert_allclose(sample_weight_fit, sample_weight_original)
@pytest.mark.parametrize("shape", ["1d", "2d"])
def test_get_feature_names_out(shape):
"""Check `get_feature_names_out` for `IsotonicRegression`."""
X = np.arange(10)
if shape == "2d":
X = X.reshape(-1, 1)
y = np.arange(10)
iso = IsotonicRegression().fit(X, y)
names = iso.get_feature_names_out()
assert isinstance(names, np.ndarray)
assert names.dtype == object
assert_array_equal(["isotonicregression0"], names)
def test_isotonic_regression_output_predict():
"""Check that `predict` does return the expected output type.
We need to check that `transform` will output a DataFrame and a NumPy array
when we set `transform_output` to `pandas`.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/25499
"""
pd = pytest.importorskip("pandas")
X, y = make_regression(n_samples=10, n_features=1, random_state=42)
regressor = IsotonicRegression()
with sklearn.config_context(transform_output="pandas"):
regressor.fit(X, y)
X_trans = regressor.transform(X)
y_pred = regressor.predict(X)
assert isinstance(X_trans, pd.DataFrame)
assert isinstance(y_pred, np.ndarray)